Comparison of Three Instructional Strategies in Teaching Programming: Restudying Material, Testing and Worked Example

Mustafa Tepgeç, Yasemin Demiraslan Çevik

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The aim of the study is to determine the effects of different instructional strategies on retention performance and cognitive load in teaching programming. The study also aimed to compare these strategies in terms of instructional efficiency. The study group consisted of 106 students (49 female and 57 male) enrolled in the first grade at a high school. Instructional strategies used in the study are testing (n=38), restudying material (n=31) and studying worked example with self-explanation prompts (n=37). In the implementation process, the study booklet was first presented to all groups. The booklet prepared for this study covers topics such as variable identification, decision structures, pseudo-codes and flow charts in teaching programming basics. The booklet was presented to restudying group for three times and they were expected to study material in depth for each session. Subsequently, isomorphic problems were presented for testing group. In the other group, worked examples were presented and learners were expected to comprehend the logic underlying the problems. Immediately after the implementation, the first retention test and the cognitive load scale were applied. The final retention test was conducted three weeks later the first retention test was implemented. The study concluded that worked example with self-explanation prompts is more efficient than the other two strategies in teaching programming basics in terms of instructional efficiency. In addition, the fact that testing has increased the long-term retention of knowledge has been confirmed. However, when cognitive load levels were taken into account, there was no difference between the testing and the restudying material strategies. It is expected that the study will contribute to the literature due to the findings in regard to pedagogy of programming.

RECEIVED 15 June 2018, REVISED 23 June 2018, ACCEPTED 26 June 2018


testing effect; teaching programming; worked example; self-explanation; programming pedagogy; cognitive load

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Abdul-Rahman, S. S., & Du Boulay, B. (2014). Learning programming via worked-examples: Relation of learning styles to cognitive load. Computers in Human Behavior, 30, 286-298.

Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of educational research, 70(2), 181-214.

Bergersen, G. R., & Gustafsson, J. E. (2011). Programming skill, knowledge, and working memory among professional software developers from an investment theory perspective. Journal of Individual Differences.

Butler, A. C., & Roediger, H. L. (2007). Testing improves long-term retention in a simulated classroom setting. European Journal of Cognitive Psychology, 19(4-5), 514-527.

Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self‐explanations: How students study and use examples in learning to solve problems. Cognitive science, 13(2), 145-182.

Demiraslan Çevik, Y., & Çoban, T. (2016). Testing effect in learning digital property and cyber ethics. SDU International Journal of Educational Studies, 3(1), 84-99.

Hoogerheide, V., Renkl, A., Fiorella, L., Paas, F., & van Gog, T. (2018). Enhancing example-based learning: Teaching on video increases arousal and improves problem-solving performance. Journal of Educational Psychology.

Johnson, C. I., & Mayer, R. E. (2009). A testing effect with multimedia learning. Journal of Educational Psychology, 101(3), 621.

Kılıç, E., & Karadeniz, Ş. (2004). Hiper ortamlarda öğrencilerin bilişsel yüklenme ve kaybolma düzeylerinin belirlenmesi. Kuram ve Uygulamada Egitim Yönetimi Dergisi, 10(4), 562-579.

Leahy, W., Hanham, J., & Sweller, J. (2015). High element interactivity information during problem solving may lead to failure to obtain the testing effect. Educational Psychology Review, 27(2), 291-304.

Lee, N. (2013). The Effects of Self-Explanation and Reading Questions and Answers on Learning Computer Programming Language (Unpublished Doctoral Dissertation). University of Nevada, Las Vegas.

Margulieux, L. E., Catrambone, R., & Guzdial, M. (2016). Employing subgoals in computer programming education. Computer Science Education, 26(1), 44-67.

Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42, 58-71.

Mayer, R. E. (2013). Teaching and learning computer programming: Multiple research perspectives. Routledge.

Paas, F. G., & Van Merriënboer, J. J. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors: The Journal of the Human Factors and Ergonomics Society, 35(4), 737-743.

Renkl, A. (2014). Toward an instructionally oriented theory of example‐based learning. Cognitive science, 38(1), 1-37.

Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer science education, 13(2), 137-172.

Roediger, H. L., & Karpicke, J. D. (2006a). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1, 181–210.

Roediger, H. L., & Karpicke, J. D. (2006b). Test-enhanced learning – Taking memory tests improves long-term retention. Pyschological Science, 17(3), 249-255. Doi: 10.1111/j.1467-9280.2006.01693.x

Si, J., Kim, D., & Na, C. (2014). Adaptive Instruction to Learner Expertise with Bimodal Process-oriented Worked-out Examples. Educational Technology & Society, 17(1), 259-27.

Skinner, B. F. (2016). The technology of teaching. BF Skinner Foundation.

Sweller, J. (1989). Cognitive technology: Some procedures for facilitating learning and problem solving in mathematics and science. Journal of educational psychology, 81(4), 457.

Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational psychology review, 22(2), 123-138.

Terry, W. S. (2011). Öğrenme & Bellek: Temel İlkeler, Süreçler ve İşlemler. Çev., Banu Cangöz. Ankara: Anı Yayınları.

Uçar, B. & Demiraslan Çevik, Y. (2018). Test Etkisinde Farklı Öğrenme Koşullarının Etkisi: Güvenli İnternet Kullanımı Konusu Örneği. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 7(1), 29-66.

Van Gog, T., & Kester, L. (2012). A test of the testing effect: acquiring problem‐solving skills from worked examples. Cognitive Science, 36(8), 1532-1541.

Van Gog, T., Kester, L., Dirkx, K., Hoogerheide, V., Boerboom, J., & Verkoeijen, P. P. (2015). Testing after worked example study does not enhance delayed problem-solving performance compared to restudy. Educational Psychology Review, 27(2), 265-289.

Van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155-174.

Vihavainen, A., Airaksinen, J., & Watson, C. (2014, July). A systematic review of approaches for teaching introductory programming and their influence on success. In Proceedings of the tenth annual conference on International computing education research (pp. 19-26). ACM.


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Journal of Learning and Teaching in Digital Age. All rights reserved, 2016. ISSN:2458-8350